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Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping

Chi-Keung Luk, Sunpyo Hong, Hyesoon Kim

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 8800 GTX, Programming techniques

January 12, 2011 by hgpu

Inertial-aided KLT feature tracking for a moving camera

Myung Hwangbo, Jun-Sik Kim, Takeo Kanade

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce 8800 Ultra, nVidia GeForce GTX 280, Video tracking

January 12, 2011 by hgpu

Using graphics processing units to generate random numbers

S. Hissoiny, P. Despres, B. Ozell

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Tags: CUDA, nVidia, nVidia GeForce GTX 280, Physics, Pseudo-random number generators

January 12, 2011 by hgpu

Performance potential for simulating spin models on GPU

Martin Weigel

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Tags: Computational Physics, CUDA, Disordered Systems and Neural Networks, Heisenberg model, High Energy Physics - Lattice, Ising model, Monte Carlo simulation, nVidia, nVidia GeForce GTX 480, Package, Physics, Statistical Mechanics, Tesla C1060

January 12, 2011 by hgpu

Understanding the design trade-offs among current multicore systems for numerical computations

Seunghwa Kang, David A. Bader, Richard W. Vuduc

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Tags: Cell processor, Computer science, CUDA, nVidia, Performance, Tesla C1060

January 11, 2011 by hgpu

A memory optimization technique for software-managed scratchpad memory in GPUs

Maryam Moazeni, Alex A. T. Bui, Majid Sarrafzadeh

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Tags: Computer science, CUDA, Memory, nVidia, nVidia Quadro FX 5600, Optimization

January 11, 2011 by hgpu

Optimization of tele-immersion codes

Albert Sidelnik, I-Jui Sung, Wanmin Wu, Maria Jesus Garzaran, Wen-mei Hwu, Klara Nahrstedt, David Padua, and Sanjay J. Patel

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 280, Optimization

January 11, 2011 by hgpu

SCELib3.0: The new revision of SCELib, the parallel computational library of molecular properties in the Single Center Approach

N. Sanna, I. Baccarelli, G. Morelli

Tags: Biology, Chemistry, CUDA, Molecular modeling, nVidia

January 11, 2011 by hgpu

iNFAnt: NFA pattern matching on GPGPU devices

Niccolo' Cascarano, Pierluigi Rolando, Fulvio Risso, Riccardo Sisto

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Tags: Computer science, CUDA, Networks, nVidia, nVidia GeForce GTX 260, String matching

January 11, 2011 by hgpu

Accelerating linpack with CUDA on heterogenous clusters

Massimiliano Fatica

Tags: Computer science, CUDA, GPU cluster, Linear Algebra, nVidia

January 11, 2011 by hgpu

FCUDA: Enabling Efficient Compilation of CUDA Kernels onto FPGAs

Ros Papakonstantinou, Karthik Gururaj, John A. Stratton, Deming Chen, Jason Cong, Wen-mei W. Hwu

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Tags: Compilers, Computer science, CUDA, FPGA, High-level Languages

January 11, 2011 by hgpu

CUDA-based real time surgery simulation

Youquan Liu, Suvranu De

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Tags: CUDA, Medicine, nVidia, nVidia GeForce 8800 GTX, Surgical simulation

January 11, 2011 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping

Inertial-aided KLT feature tracking for a moving camera

Using graphics processing units to generate random numbers

Performance potential for simulating spin models on GPU

Understanding the design trade-offs among current multicore systems for numerical computations

A memory optimization technique for software-managed scratchpad memory in GPUs

Optimization of tele-immersion codes

SCELib3.0: The new revision of SCELib, the parallel computational library of molecular properties in the Single Center Approach

iNFAnt: NFA pattern matching on GPGPU devices

Accelerating linpack with CUDA on heterogenous clusters

FCUDA: Enabling Efficient Compilation of CUDA Kernels onto FPGAs

CUDA-based real time surgery simulation

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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